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Communication Dans Un Congrès Année : 2015

Local sparse representation based Interest Point matching for person re-identification

Résumé

This paper presents a multi-shot person re-identification system from video sequences based on Interest Points (SURFs) matching. Our objective is to improve the Interest Points (IPs) matching using low resolution images in terms of re-identification accuracy and running time. First, we propose a new method of SURF matching via Local Sparse Representation (LSR). Each SURF in the test video sequence is expressed as a sparse representation of a subset of SURFs in the reference dataset. Our approach consists of searching the latter subset from the reference IPs that are located on a similar spatial neighborhood to the query IP. Second, it investigates whether IPs filtering can decrease the re-identification running time. An ensemble of binary classifiers are evaluated. Our approach is assessed on the large dataset PRID-2011 and shown to outperform favorably with current state of the art
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Dates et versions

hal-01262553 , version 1 (26-01-2016)

Identifiants

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Mohamed Ibn Khedher, Mounim El Yacoubi. Local sparse representation based Interest Point matching for person re-identification. ICONIP 2015 : 22nd International Conference on Neural Information Processing, Nov 2015, Istanbul, Turkey. pp.241 - 250, ⟨10.1007/978-3-319-26555-1_28⟩. ⟨hal-01262553⟩
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